Accelerating Biomedical Signal Processing Using GPU: A Case Study of Snore Sound Feature Extraction

• Published in: Interdisciplinary sciences : computational life sciences Vol. 9; no. 4; pp. 550 - 555
• Main Authors: Guo, Jian, Qian, Kun, Zhang, Gongxuan, Xu, Huijie, Schuller, Björn
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2017; Springer Nature B.V
• Subjects: Algorithms; Biomedical and Life Sciences; Biomedical data; Case studies; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Appl. in Life Sciences; Data analysis; Data management; Data processing; Feature extraction; Graphics processing units; Health Sciences; Humans; Learning algorithms; Life Sciences; Machine Learning; Mathematical and Computational Physics; Medicine; Original Research Article; Signal processing; Snoring; Sound; Statistics for Life Sciences; Theoretical; Theoretical and Computational Chemistry; Signal processing; Feature extraction; GPU; Biomedical; Python

The advent of ‘Big Data’ and ‘Deep Learning’ offers both, a great challenge and a huge opportunity for personalised health-care. In machine learning-based biomedical data analysis, feature extraction is a key step for ‘feeding’ the subsequent classifiers. With increasing numbers of biomedical data,...